1. Technical Field
Embodiments of the present invention relate generally to heath care management. More particularly, embodiments of the present invention relate to efficient provision of healthcare by analyzing healthcare provider supply and patient demand.
2. Background
Healthcare has become a central issue today. For example, recently Congress passed sweeping healthcare reform aimed at providing access to healthcare for millions of people uncovered under the previous healthcare paradigms.
Despite increased access to healthcare, patients often face long waits and must endure healthcare providers not performing optimally. These waits and substantial performance issues can often be traced to poorly managed healthcare systems. The poor management is due, in large part, to lack of tools to efficiently allocate healthcare resources, such as doctors, nurses, equipment, and rooms to patients requiring these services. As a result, conventional healthcare provision is often highly inefficient. This inefficiency can have devastating results. Not only does the inefficiency result in substantially higher healthcare costs, but patient condition can worsen due to the inefficiencies, even to the point of death.
One such inefficiency results from a healthcare provider's limited capacity. Limited capacity can arise in a number of forms. For example, limited capacity can manifest itself as a result of limited knowledge. However, limited capacity is more often the result of the healthcare provider becoming overwhelmed by case load. Whether a healthcare provider is responsible for too many patients, a reasonable number of patients that require differing amounts of care at the same time, or a reasonable number of patients, where some cases increase in complexity, a healthcare provider can provide only so much care at a particular quality level before the quality level begins to decrease.
Other factors also affect healthcare costs and provider efficiency. For example, length of stay (“LOS”) tends to have a significant impact of healthcare cost and efficiency. LOS metrics can be used to correlate or predict a patient's expected LOS for a given diagnosis. In addition, a healthcare provider can gather its own LOS metrics. For example, a healthcare provider can computer an arithmetic mean length of stay (“AMLOS”) for each patient and/or those patients having a particular diagnosis. The patients' actual LOS is then compared against this benchmark. Problems can be indicated when LOS is greater than the LOS predicted by AMLOS.
In addition to LOS, the number of diagnoses for a particular patient has an effect on healthcare costs. For example, a patient diagnosed with congestive heart failure (“CHF”) has an expected cost. However that cost will likely rise in an exponential manner if there are additional diagnoses, for example, pneumonia. As is well known, many patients are often associated with a number of diagnoses. In fact, both Medicare studies and others (including Dr. Don Berwick of the Institute of Healthcare Improvement) have noted that approximately 70% of costs can be attributed to just 10% of patients.